A semantic ontology for representing and quantifying energy flexibility of buildings

Abstract

• EFOnt, a semantic ontology for building energy flexibility applications, is introduced. • EFOnt is designed to work with other building energy data tools as part of an ecosystem • EFOnt standardizes energy flexibility definition, characterization, and quantification. • One measurement-based and one simulation-based use case of EFOnt are given. • The future development and potential use cases of EFOnt are discussed. Energy flexibility of buildings can be an essential resource for a sustainable and reliable power grid with the growing variable renewable energy shares and the trend to electrify and decarbonize buildings. Traditional demand-side management technologies, advanced building controls, and emerging distributed energy resources (including electric vehicle, energy storage, and on-site power generation) enable the transition of the building stock to grid-interactive efficient buildings (GEBs) that operate efficiently to meet service needs and are responsive to grid pricing or carbon signals to achieve energy and carbon neutrality. Although energy flexibility has received growing attention from industry and the research community, there remains a lack of common ground for energy flexibility terminologies, characterization, and quantification methods. This paper presents a semantic ontology—EFOnt (Energy Flexibility Ontology)—that extends existing terminologies, ontologies, and schemas for building energy flexibility applications. EFOnt aims to serve as a standardized tool for knowledge co-development and streamlining energy flexibility related applications. We demonstrate potential use cases of EFOnt via two examples: ( 1 ) energy flexibility analytics with measured data from a residential smart thermostat dataset and a commercial building, and ( 2 ) modeling and simulation to evaluate energy flexibility of buildings. The compatibility of EFOnt with existing ontologies and the outlook of EFOnt's role in the building energy data tool ecosystem are discussed.